📄 Mya Mymr¶

Belebele is a multiple-choice machine reading comprehension (MRC) dataset spanning 122 language variants. This dataset enables the evaluation of mono- and multi-lingual models in high-, medium-, and low-resource languages. Each question has four multiple-choice answers and is linked to a short passage from the FLORES-200 dataset. The human annotation procedure was carefully curated to create questions that… See the full description on the dataset page: https://huggingface.co/datasets/facebook/belebele.

Tags: arxiv:2308.16884, language:['af', 'am', 'ar', 'az', 'as', 'bm', 'bn', 'bo', 'bg', 'ca', 'cs', 'ku', 'da', 'de', 'el', 'en', 'es', 'et', 'eu', 'fi', 'fr', 'ff', 'om', 'gu', 'gn', 'ht', 'ha', 'he', 'hi', 'hr', 'hu', 'hy', 'ig', 'id', 'it', 'is', 'jv', 'ja', 'ka', 'kn', 'kk', 'mn', 'km', 'rw', 'ky', 'ko', 'lo', 'ln', 'lt', 'lg', 'lv', 'ml', 'mr', 'mk', 'mt', 'mi', 'my', 'nl', 'no', 'ne', 'ny', 'or', 'pa', 'ps', 'fa', 'mg', 'pl', 'pt', 'ro', 'ru', 'sn', 'si', 'sl', 'sv', 'sk', 'sd', 'sw', 'ta', 'te', 'tg', 'tl', 'th', 'ti', 'tn', 'ts', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yo', 'zh', 'ms', 'zu'], license:cc-by-sa-4.0, region:us, size_categories:100K<n<1M, task_categories:['question-answering', 'zero-shot-classification', 'text-classification', 'multiple-choice'], category:dataset

cards.belebele.mya_mymr

type: TaskCard
loader: 
  type: LoadHF
  path: facebook/belebele
  name: mya_Mymr
preprocess_steps: 
  - type: ListFieldValues
    fields: 
      - mc_answer1
      - mc_answer2
      - mc_answer3
      - mc_answer4
    to_field: choices
  - type: Rename
    field_to_field: 
      correct_answer_num: answer
      flores_passage: context
  - type: CastFields
    fields: 
      answer: int
  - type: AddConstant
    field: answer
    add: -1
  - type: Set
    fields: 
      context_type: passage
task: tasks.qa.multiple_choice.with_context
templates: templates.qa.multiple_choice.with_context.no_intro.all
[source]

Explanation about TaskCard¶

TaskCard delineates the phases in transforming the source dataset into model input, and specifies the metrics for evaluation of model output.

Args:

loader:
specifies the source address and the loading operator that can access that source and transform it into a unitxt multistream.

preprocess_steps:
list of unitxt operators to process the data source into model input.

task:
specifies the fields (of the already (pre)processed instance) making the inputs, the fields making the outputs, and the metrics to be used for evaluating the model output.

templates:
format strings to be applied on the input fields (specified by the task) and the output fields. The template also carries the instructions and the list of postprocessing steps, to be applied to the model output.

default_template:
a default template for tasks with very specific task dataset specific template

Explanation about ListFieldValues¶

Concatenates values of multiple fields into a list, and assigns it to a new field.

Explanation about Set¶

Sets specified fields in each instance, in a given stream or all streams (default), with specified values. If fields exist, updates them, if do not exist – adds them.

Args:
fields (Dict[str, object]): The fields to add to each instance. Use ‘/’ to access inner fields

use_deepcopy (bool) : Deep copy the input value to avoid later modifications

Examples:
# Set a value of a list consisting of “positive” and “negative” do field “classes” to each and every instance of all streams Set(fields={"classes": ["positive","negatives"]})

# In each and every instance of all streams, field “span” is to become a dictionary containing a field “start”, in which the value 0 is to be set Set(fields={"span/start": 0}

# In all instances of stream “train” only, Set field “classes” to have the value of a list consisting of “positive” and “negative” Set(fields={"classes": ["positive","negatives"], apply_to_stream=["train"]})

# Set field “classes” to have the value of a given list, preventing modification of original list from changing the instance. Set(fields={"classes": alist}), use_deepcopy=True) if now alist is modified, still the instances remain intact.

Explanation about CastFields¶

Casts specified fields to specified types.

Args:

fields (Dict[str, str]):
A dictionary mapping field names to the names of the types to cast the fields to. e.g: “int”, “str”, “float”, “bool”. Basic names of types

defaults (Dict[str, object]):
A dictionary mapping field names to default values for cases of casting failure.

process_every_value (bool):
If true, all fields involved must contain lists, and each value in the list is then casted. Defaults to False.

Example:
CastFields(
    fields={"a/d": "float", "b": "int"},
    failure_defaults={"a/d": 0.0, "b": 0},
    process_every_value=True,
)
would process the input instance: {"a": {"d": ["half", "0.6", 1, 12]}, "b": ["2"]} into {"a": {"d": [0.0, 0.6, 1.0, 12.0]}, "b": [2]}.

Explanation about Rename¶

Renames fields.

Move value from one field to another, potentially, if field name contains a /, from one branch into another. Remove the from field, potentially part of it in case of / in from_field.

Examples:
Rename(field_to_field={“b”: “c”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “c”: 2}, {“a”: 2, “c”: 3}]

Rename(field_to_field={“b”: “c/d”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “c”: {“d”: 2}}, {“a”: 2, “c”: {“d”: 3}}]

Rename(field_to_field={“b”: “b/d”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “b”: {“d”: 2}}, {“a”: 2, “b”: {“d”: 3}}]

Rename(field_to_field={“b/c/e”: “b/d”}) will change inputs [{“a”: 1, “b”: {“c”: {“e”: 2, “f”: 20}}}] to [{“a”: 1, “b”: {“c”: {“f”: 20}, “d”: 2}}]

Explanation about AddConstant¶

Adds a constant, being argument ‘add’, to the processed value.

Args:
add: the constant to add.

Explanation about LoadHF¶

Loads datasets from the HuggingFace Hub.

It supports loading with or without streaming, and it can filter datasets upon loading.
Args:

path:
The path or identifier of the dataset on the HuggingFace Hub.

name:
An optional dataset name.

data_dir:
Optional directory to store downloaded data.

split:
Optional specification of which split to load.

data_files:
Optional specification of particular data files to load.

revision:
Optional. The revision of the dataset. Often the commit id. Use in case you want to set the dataset version.

streaming (bool):
indicating if streaming should be used.

filtering_lambda (str, optional):
A lambda function for filtering the data after loading.

num_proc (int, optional):
Specifies the number of processes to use for parallel dataset loading.

Example:
Loading glue’s mrpc dataset
load_hf = LoadHF(path='glue', name='mrpc')

References: templates.qa.multiple_choice.with_context.no_intro.all, tasks.qa.multiple_choice.with_context